期刊文献+
共找到2,449篇文章
< 1 2 123 >
每页显示 20 50 100
An Algorithm for the Inverse Problem of Matrix Processing: DNA Chains, Their Distance Matrices and Reconstructing
1
作者 Boris F. Melnikov Ye Zhang Dmitrii Chaikovskii 《Journal of Biosciences and Medicines》 CAS 2023年第5期310-320,共11页
We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is forme... We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains, as well as any specific object of study. At the same time, for example, the practical programming results show that on an average modern computer, it takes about a day to build such a 30 × 30 matrix for mnDNAs using the Needleman-Wunsch algorithm;therefore, for such a 300 × 300 matrix, about 3 months of continuous computer operation is expected. Thus, even for a relatively small number of species, calculating the distance matrix on conventional computers is hardly feasible and the supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains, then we publish algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. Previously, we used the method of branches and boundaries, but in this paper we propose to use another new algorithm for restoring the distance matrix for DNA chains. Our recent work has shown that even greater improvement in the quality of the algorithm can often be achieved without improving the auxiliary heuristics of the branches and boundaries method. Thus, we are improving the algorithms that formulate the greedy function of this method only. . 展开更多
关键词 DNA Chains distance Matrix Optimization Problem Restoring algorithm Greedy algorithm HEURISTICS
下载PDF
A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data
2
作者 Yisa Adeniyi Abolade Yichuan Zhao 《Open Journal of Modelling and Simulation》 2024年第2期33-42,共10页
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode... Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance. 展开更多
关键词 Compositional Data Linear Regression Model Least Square Method Robust Least Square Method Synthetic Data Aitchison distance Maximum Likelihood Estimation Expectation-Maximization algorithm k-Nearest Neighbor and Mean imputation
下载PDF
基于Max-min distance聚类算法的园地空间聚类--以永泰县嵩口镇为例
3
作者 冯宇琳 《测绘与空间地理信息》 2024年第7期146-149,共4页
空间聚类是空间数据挖掘的重要手段之一。本文研究了一种基于质心点距离的Max-min distance空间聚类算法:通过加载园地图斑数据,计算其园地图斑质心,判断聚类中心之间的距离,并将符合条件的园地图斑进行聚类,最终将聚类结果可视化表达... 空间聚类是空间数据挖掘的重要手段之一。本文研究了一种基于质心点距离的Max-min distance空间聚类算法:通过加载园地图斑数据,计算其园地图斑质心,判断聚类中心之间的距离,并将符合条件的园地图斑进行聚类,最终将聚类结果可视化表达。本文的算法是利用Visual Studio 2017实验平台和ArcGIS Engine组件式开发环境,采用C#语言进行编写。实验结果表明:1)Max-mindistance聚类通过启发式的选择簇中心,克服了K-means选择簇中心过于邻近的缺点,能够适应嵩口镇等山区丘陵地区空间分布呈破碎的园地数据集分布,有效地实现园地的合理聚类;2)根据连片面积将园地空间聚类结果分为大中小三类,未来嵩口镇可以重点发展园地连片规模较大的村庄,形成规模化的青梅种植园。 展开更多
关键词 Max-mindistance聚类算法 园地 GIS 嵩口镇
下载PDF
An Improved DV-Hop Localization Algorithm Based on Hop Distances Correction 被引量:9
4
作者 Guiqi Liu Zhihong Qian Xue Wang 《China Communications》 SCIE CSCD 2019年第6期200-214,共15页
DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown ... DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown node.So an improved DV-Hop localization algorithm based on correctional average size of a hop,HDCDV-Hop algorithm,is proposed.The improved algorithm corrects the estimated distance between the unknown node and different anchor nodes based on fractional hop count information and relatively accurate coordinates of the anchor nodes information,and it uses the improved Differential Evolution algorithm to get the estimate location of unknown nodes so as to further reduce the localization error.Simulation results show that our proposed algorithm have lower localization error and higher localization accuracy compared with the original DV-Hop algorithm and other classical improved algorithms. 展开更多
关键词 WSN DV-HOP localization algorithm HOP distance CORRECTION IMPROVED Differential Evolution algorithm
下载PDF
Distance Concentration-Based Artificial Immune Algorithm 被引量:6
5
作者 LIUTao WANGYao-cai +1 位作者 WANGZhi-jie MENGJiang 《Journal of China University of Mining and Technology》 EI 2005年第2期81-85,共5页
The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion... The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune al- gorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the prob- lem of precocity,holding the diversity of antibody, and enhancing convergence rate. 展开更多
关键词 artificial immune system distance concentration immune algorithm
下载PDF
Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms 被引量:1
6
作者 Fang Bingyi Wu Siliang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第1期76-80,共5页
An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measur... An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method. 展开更多
关键词 miss distance 2-D assignment auction algorithm data association
下载PDF
A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm 被引量:4
7
作者 Junfei Qiao Fei Li +2 位作者 Cuili Yang Wenjing Li Ke Gu 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2020年第1期276-291,共16页
Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a dis... Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors. 展开更多
关键词 distance concentration immune algorithm(DCIA) information processing strength(IPS) radial basis function neural network(RBFNN)
下载PDF
Distance Control Algorithm for Automobile Automatic Obstacle Avoidance and Cruise System
8
作者 Jinguo Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2018年第7期69-88,共20页
With the improvement of automobile ownership in recent years,the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher.As science and technology... With the improvement of automobile ownership in recent years,the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher.As science and technology develops constantly,the development of automobile automatic obstacle avoidance and cruise system accelerates gradually,and the requirement on distance control becomes stricter.Automobile automatic obstacle avoidance and cruise system can determine the conditions of automobiles and roads using sensing technology,automatically adopt measures to control automobile after discovering road safety hazards,thus to reduce the incidence of traffic accidents.To prevent accidental collision of automobile which are installed with automatic obstacle avoidance and cruise system,active brake should be controlled during driving.This study put forward a neural network based proportional-integral-derivative(PID)control algorithm.The active brake of automobiles was effectively controlled using the system to keep the distance between automobiles.Moreover the algorithm was tested using professional automobile simulation platform.The results demonstrated that neural network based PID control algorithm can precisely and efficiently control the distance between two cars.This work provides a reference for the development of automobile automatic obstacle avoidance and cruise system. 展开更多
关键词 OBSTACLE AVOIDANCE and CRUISE distance control AUTOMOBILE algorithm
下载PDF
Distance function selection in several clustering algorithms
9
作者 LUYu 《Journal of Chongqing University》 CAS 2004年第1期47-50,共4页
Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical... Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical clustering were investigated. Both theoretical analysis and detailed experimental results were given. It is shown that a distance function greatly affects clustering results and can be used to detect the outlier of a cluster by the comparison of such different results and give the shape information of clusters. In practice situation, it is suggested to use different distance function separately, compare the clustering results and pick out the 搒wing points? And such points may leak out more information for data analysts. 展开更多
关键词 distance function clustering algorithms K-MEANS DENDROGRAM data mining
下载PDF
Transmission Lines Distance Protection Using Differential Equation Algorithm and Hilbert-Huang Transform
10
作者 Xingmao Liu Zhengyou He 《Journal of Power and Energy Engineering》 2014年第4期616-623,共8页
This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various fa... This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various factors, such as the distributed capacitance, the transient response characteristics of current transformer and voltage transformer, etc. In order to overcome this problem, the proposed scheme applies HHT to improve the apparent impedance estimated by DEA. Empirical mode decomposition (EMD) is used to decompose the data set from DEA into the intrinsic mode functions (IMF) and the residue. This residue has monotonic trend and is used to evaluate the impedance of faulty line. Simulation results show that the proposed scheme improves significantly the accuracy of the estimated impedance. 展开更多
关键词 Hilbert-Huang TRANSFORM DIFFERENTIAL EQUATION algorithm distance PROTECTION Transmission LINES
下载PDF
Prediction-Based Distance Weighted Algorithm for Target Tracking in Binary Sensor Network
11
作者 SUN Xiaoyan LI Jiandong +1 位作者 CHEN Yanhui HUANG Pengyu 《China Communications》 SCIE CSCD 2010年第4期41-50,共10页
Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algori... Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property. 展开更多
关键词 Binary Sensor Network Weighted algorithm Particle Filter distance Weight Recursive Least Squre(RLS)
下载PDF
Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction
12
作者 陈建林 李磊 +5 位作者 王林元 蔡爱龙 席晓琦 张瀚铭 李建新 闫镔 《Chinese Physics B》 SCIE EI CAS CSCD 2015年第2期513-520,共8页
The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterat... The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model(DDM) is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU(graphic processing unit) platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction. 展开更多
关键词 computed tomography iterative reconstruction parallelizable algorithm distance-driven model
下载PDF
Mesh‑free semi‑quantitative variance underestimation elimination method in Monte Caro algorithm
13
作者 Peng‑Fei Shen Xiao‑Dong Huo +4 位作者 Ze‑Guang Li Zeng Shao Hai‑Feng Yang Peng Zhang Kan Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第1期157-171,共15页
The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This stu... The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This study provides a mesh-free semiquantitative variance underestimation elimination method to obtain a credible confidence interval for the tallied results.This method comprises two procedures:Estimation and Elimination.The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm.The batch method was then used in the elimination procedure.The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem.We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model.The results showed that the average variance underestimation ratios of local tallies declined from 37 to 87%to within±5%in these models. 展开更多
关键词 Monte Carlo algorithm Power iteration process Inter-cycle correlation Variance underestimation Sliced Wasserstein distance
下载PDF
Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks
14
作者 VDinesh SSrinivasan +1 位作者 Gyanendra Prasad Joshi Woong Cho 《Computer Systems Science & Engineering》 SCIE EI 2023年第7期687-699,共13页
In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections... In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches. 展开更多
关键词 Vehicular networks CLUSTERING evolutionary algorithm fitness function distance metric
下载PDF
高速铁路动静态轨检数据里程对齐与误差修正 被引量:1
15
作者 何庆 马玉松 +3 位作者 李晨钟 俞伟东 吴维军 王平 《铁道学报》 EI CAS CSCD 北大核心 2024年第1期129-136,共8页
轨道几何动、静检测数据间的精确匹配对探明高速铁路线路服役状态和制定准确可靠的养护维修策略具有关键作用。针对动静里程匹配算法研究较少的现状,提出利用动、静态实测数据波形匹配,建立基于互相关函数与动态时间规划相结合的两阶段... 轨道几何动、静检测数据间的精确匹配对探明高速铁路线路服役状态和制定准确可靠的养护维修策略具有关键作用。针对动静里程匹配算法研究较少的现状,提出利用动、静态实测数据波形匹配,建立基于互相关函数与动态时间规划相结合的两阶段修正算法,并以距离误差作为评价指标。结合某高铁线路轨道几何检测数据的案例分析,以静态检测数据作为参考基准,对动态检测数据进行里程误差评估与修正。结果表明,两阶段算法修正效果显著,累积距离误差降幅超过93%,修正后的动、静态检测数据严格对齐保证了动态检测数据的可靠性与有效性。 展开更多
关键词 高速铁路 里程误差修正 动静轨检数据 两阶段修正算法 距离误差
下载PDF
改进A^(*)算法的移动机器人全局路径规划 被引量:1
16
作者 熊勇刚 李波 +2 位作者 姚焘 付茂林 李城炫 《电子测量技术》 北大核心 2024年第5期31-36,共6页
针对A^(*)算法在移动机器人路径规划存在搜索效率低,路径斜穿障碍物顶点,路径拐弯多等问题。提出一种改进的A^(*)算法,首先在A^(*)算法的邻域扩展中采用避免斜穿障碍物顶点的策略;再引入障碍物因素对评价函数进行指数加权,减少不必要的... 针对A^(*)算法在移动机器人路径规划存在搜索效率低,路径斜穿障碍物顶点,路径拐弯多等问题。提出一种改进的A^(*)算法,首先在A^(*)算法的邻域扩展中采用避免斜穿障碍物顶点的策略;再引入障碍物因素对评价函数进行指数加权,减少不必要的搜索,提高A^(*)算法的效率和灵活性,使算法偏向于选择障碍物较少的路径;最后使用三次优化折线的策略,加入障碍物安全距离,减少路径上的冗余节点和拐弯。使用MATLAB进行实验仿真,结果表明,在20 m×20 m、40 m×40 m、60 m×60 m栅格地图环境下,改进A^(*)算法较传统A^(*)算法,搜索时间分别减少70.12%、84.31%、91.44%,扩展节点分别减少53.77%、71.20%、74.30%,路径累计拐弯角度分别减少70.48%、76.31%、82.18%,改进A^(*)算法能够有效的提高移动机器人路径规划的效率,路径更为平滑和安全,且在复杂环境中优势更为明显。 展开更多
关键词 A^(*)算法 评价函数 领域扩展 安全距离 路径规划
下载PDF
基于PSO-CNN-XGBoost水下柱形装药峰值超压预测 被引量:1
17
作者 刘芳 李士伟 +1 位作者 卢熹 郭策安 《兵工学报》 EI CAS CSCD 北大核心 2024年第5期1602-1612,共11页
为探索水下柱形装药结构、爆距等参数与水下柱形装药峰值超压的关系,将装药样本数据视为二维数据,建立粒子群优化(Particle Swarm Optimization,PSO)算法、一维卷积神经网络(1D Convolutional Neural Network,1DCNN)和极端梯度提升(Extr... 为探索水下柱形装药结构、爆距等参数与水下柱形装药峰值超压的关系,将装药样本数据视为二维数据,建立粒子群优化(Particle Swarm Optimization,PSO)算法、一维卷积神经网络(1D Convolutional Neural Network,1DCNN)和极端梯度提升(Extreme Gradient Boosting,XGBoost)的水下柱形装药峰值超压融合预测算法。采用相关性分析与数据可视化方法,分析装药结构参数、爆距与峰值超压之间的关联关系。设计1DCNN深度网络挖掘不同长径比、爆距等参数与峰值超压之间的纵向时序关系。运用XGBoost算法寻找装药结构参数、爆距与峰值超压之间的横向非线性关系,提升小样本数据的预测精度。使用PSO算法优化1DCNN和XGBoost的超参数,获得最优算法结构。研究结果表明,在包含10种智能算法的对比实验中,PSO-CNN-XGBoost水下柱形装药峰值超压预测算法在精度、稳定性、拟合程度上均高于其他模型。 展开更多
关键词 水下柱形装药 长径比 爆距 峰值超压 粒子群优化算法 一维卷积神经网络 极端梯度提升
下载PDF
基于隐式偏好的多目标推荐算法研究 被引量:1
18
作者 陈宏 王丽萍 +2 位作者 翁杭立 祝俊毅 郭海东 《小型微型计算机系统》 CSCD 北大核心 2024年第4期830-837,共8页
推荐的准确性(accuracy)和多样性(diversity)是推荐算法研究的二个重要指标,能够最大程度地满足用户的喜好.然而,基于准确性的推荐将导致推荐结果过于聚焦集中在某类特征上,使得多样性降低,导致用户选择的广度不足而整体效果不佳.针对... 推荐的准确性(accuracy)和多样性(diversity)是推荐算法研究的二个重要指标,能够最大程度地满足用户的喜好.然而,基于准确性的推荐将导致推荐结果过于聚焦集中在某类特征上,使得多样性降低,导致用户选择的广度不足而整体效果不佳.针对推荐算法的两个指标之间的平衡以满足用户的需求,本文采用最大预测评分和最大内部相似度差异的两目标模型,选取极值点和膝点为隐式偏好,利用隐式偏好改进推荐方案搜索优化策略,提出了一种基于隐式偏好的多目标推荐算法.该算法利用切比雪夫距离在迭代过程中对偏好点动态标定,以引导个体收敛于隐式偏好区域,得到具有不同偏好的推荐方案.在Movielens和Netflix数据集上实验结果表明,与Item-based协同过滤推荐算法相比,该算法的推荐结果在确保准确率性能情况下多样性平均提升了38%和33.4%,新颖度平均提升了58.6%和125.4%,降低了多目标推荐算法的复杂度,有效解决了实际应用问题. 展开更多
关键词 推荐算法 准确性 多样性 多目标优化 隐式偏好 切比雪夫距离
下载PDF
基于累积和事件段识别与改进谱聚类的锂离子电池储能系统内短路故障检测方法 被引量:1
19
作者 肖先勇 陈智凡 +2 位作者 汪颖 何涛 张逢蓉 《电网技术》 EI CSCD 北大核心 2024年第2期658-667,共10页
锂离子电池系统的内短路故障可能导致严重安全事故,其检测受到在线检测实时性以及故障特征获得性制约,是当下锂离子电池储能系统安全运行亟待解决的问题。该文提出一种基于累积和(cumulative sum,CUSUM)事件段检测与改进谱聚类的锂离子... 锂离子电池系统的内短路故障可能导致严重安全事故,其检测受到在线检测实时性以及故障特征获得性制约,是当下锂离子电池储能系统安全运行亟待解决的问题。该文提出一种基于累积和(cumulative sum,CUSUM)事件段检测与改进谱聚类的锂离子电池储能系统内短路故障检测方法。首先,考虑内短路故障时的电压/温度变化特性,基于累积和事件突变点识别方法,识别疑似内短路故障事件段。其次,构建三维故障特征,刻画检测对象内短路故障特征属性。然后,构建基于Wasserstein测度的内短路故障特征距离矩阵,检测三维空间各点稀疏特性,客观划定故障聚类,实现内短路故障检测。搭建锂离子电池内短路实验平台、建立锂离子电池电–热耦合仿真模型,算例结果表明该文方法能够准确识别疑似内短路故障事件段,在不同串并联形式及故障类型下实现故障检测,证明了该文方法的正确性与可行性。 展开更多
关键词 内短路故障检测 事件段检测 故障特征 Wasserstein距离 改进谱聚类算法
下载PDF
附加距离约束的导航相机快速标定方法
20
作者 赵洪涛 白金国 左金凤 《遥感信息》 CSCD 北大核心 2024年第2期70-78,共9页
针对在轨条件下因观测条件匮乏导致巡视器导航相机标定精度受限的问题,提出了一种基于直线特征的导航相机快速在轨自标定方法。首先,利用SAM(segment anything model)对导航影像太阳能板区域进行图像分割,并对太阳能板OCR(optical contr... 针对在轨条件下因观测条件匮乏导致巡视器导航相机标定精度受限的问题,提出了一种基于直线特征的导航相机快速在轨自标定方法。首先,利用SAM(segment anything model)对导航影像太阳能板区域进行图像分割,并对太阳能板OCR(optical control room)直线特征进行提取;然后,构建附加距离约束的导航相机自检校模型;最后,利用Levenberg-Marquardt方法对标定模型进行求解。开展了模拟实验及在轨实验。分析结果表明,相比光束法自标定、CAHVOR及Vanishing Points等标定方法,所提出方法标定参数重建的坐标精度平均值分别提升了20.60%、30.82%和35.92%,对太阳能板OCR距离恢复距离偏差范围在-1.4~1.5 mm之间。该方法可为巡视器导航相机在轨标定技术提供一定的价值参考。 展开更多
关键词 SAM 在轨标定 光束法 LM算法 距离约束
下载PDF
上一页 1 2 123 下一页 到第
使用帮助 返回顶部